对抗样本(论文解读十一):PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning

时间 2020-12-24

标签 Deep learning 对抗样本繁體版

原文原文链接

PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning Chenglin Yang, Adam Kortylewski, Cihang Xie, Yinzhi Cao, and Alan Yuille Johns Hopkins University 通过强化学习实现一个基于纹理的黑盒攻击这是一篇比较新的

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1. 对抗样本(论文解读一): DPATCH: An Adversarial Patch Attack on Object Detectors
2. 对抗样本(论文解读五):Perceptual-Sensitive GAN for Generating Adversarial Patches
3. 对抗样本(论文解读八):Towards More Robust Adversarial Attack Against Real World Object Detectors
4. 对抗样本(论文解读七):On Physical Adversarial Patches for Object Detection
5. 对抗样本(论文解读六):Adversarial camera stickers: A physical camera-based attack on deep learning systems
6. 对抗样本(论文解读四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
7. COMA(一)： Learning to Communicate with Deep Multi-Agent Reinforcement Learning 论文讲解
8. 对抗样本(论文解读三): Adversarial Examples Improve Image Recognition
9. RL论文阅读【四】Deep Reinforcement Learning with Double Q-learning（Double DQN）
10. Adversary Attack(对抗攻击)论文阅读笔记
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1. 对抗样本(论文解读一): DPATCH: An Adversarial Patch Attack on Object Detectors
2. 对抗样本(论文解读五):Perceptual-Sensitive GAN for Generating Adversarial Patches
3. 对抗样本(论文解读八):Towards More Robust Adversarial Attack Against Real World Object Detectors
4. 对抗样本(论文解读七):On Physical Adversarial Patches for Object Detection
5. 对抗样本(论文解读六):Adversarial camera stickers: A physical camera-based attack on deep learning systems
6. 对抗样本(论文解读四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
7. COMA(一)： Learning to Communicate with Deep Multi-Agent Reinforcement Learning 论文讲解
8. 对抗样本(论文解读三): Adversarial Examples Improve Image Recognition
9. RL论文阅读【四】Deep Reinforcement Learning with Double Q-learning（Double DQN）
10. Adversary Attack(对抗攻击)论文阅读笔记

>>更多相关文章<<